// Use when the user wants to review their recent Claude Code sessions for patterns — analyzes the last N sessions (default 5) in the current project, dispatching parallel reviewers per session, then synthesizing cross-session findings
ALWAYS use this skill when writing or refactoring code. Includes context-dependent sub-skills to empower different coding styles across languages and runtimes.
Use when writing, reviewing, or modifying Rust code - covers error handling with thiserror+miette, type system patterns, async and serde conventions, testing crates, dependency pinning, and module organization
Use when the user wants to export a Claude Code session transcript as a readable Markdown file — converts the current session (or a specified transcript path) into GitHub-flavored Markdown with metadata header, collapsible tool results, and thinking blocks
Use when the user wants to review a Claude Code session for quality — analyzes the current session (or a specified transcript path) for prompting effectiveness, agent performance, and environment gaps, producing actionable recommendations
Use when writing skills, CLAUDE.md files, agent prompts, or any directives that involve shell commands, environment variables, API credentials, file creation, or git operations - prevents secrets leakage into LLM context, unsafe shell patterns, and credential exposure
Use when writing instructions that guide Claude behavior - skills, CLAUDE.md files, agent prompts, system prompts. Covers token efficiency, compliance techniques, and discovery optimization.
| name | review-recent-sessions |
| description | Use when the user wants to review their recent Claude Code sessions for patterns — analyzes the last N sessions (default 5) in the current project, dispatching parallel reviewers per session, then synthesizing cross-session findings |
Review multiple recent sessions from the current project directory to identify cross-session patterns.
ed3d-extending-claude plugin must be installed.ed3d-session-reflection plugin must be installed (provides the conversation-reviewer agent and reduce-transcript.py script).The user may invoke this as:
/review-recent-sessions — review last 5 sessions/review-recent-sessions 10 — review last 10 sessionsUse the current session's transcript path to determine the project directory. The transcript path looks like:
~/.claude/projects/-Users-ed-Development-.../SESSION_ID.jsonl
The directory containing it is the project's session directory.
If you cannot determine the project directory, ask the user.
Find the most recent JSONL files in the project directory, sorted by modification time, limited to the requested count (default 5).
ls -t "<project_session_dir>"/*.jsonl | head -<count>
Exclude the current session's transcript (the user doesn't want to review the review session itself).
If fewer than 2 sessions are found, tell the user there aren't enough sessions to do a cross-session review and suggest using /review-session instead.
Create a working directory:
mkdir -p /tmp/session-review-batch
For each session, run the reduction script:
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/reduce-transcript.py" "<session.jsonl>" "/tmp/session-review-batch/reduced-<N>.txt"
This can be done in a single bash command with a loop.
For each reduced transcript, dispatch a conversation-reviewer agent in the background:
Transcript path: /tmp/session-review-batch/reduced-N.txt Write your findings to: /tmp/session-review-batch/findings-N.md
Read the transcript, analyze it, and write your findings following your output format.
Dispatch ALL reviewers in a single message to maximize parallelism. Tell the user you've dispatched N reviewers and are waiting for results.
Once all reviewers complete, dispatch a general-purpose Sonnet agent to synthesize:
ed3d-basic-agents:sonnet-general-purpose Synthesize session reviews You are synthesizing findings from multiple Claude Code session reviews into a cross-session analysis.Read all findings files in /tmp/session-review-batch/findings-*.md
Produce a synthesis that identifies:
Recurring patterns — issues that appear across multiple sessions. These are the highest-value findings because they represent systematic problems.
Progression — is the user getting better or worse at prompting over time? Is the agent handling certain tasks better or worse?
Highest-impact recommendations — across all sessions, which recommendations would have the biggest effect? Prioritize:
Session-specific highlights — any single-session finding that's particularly noteworthy even if it didn't recur.
Write your synthesis to /tmp/session-review-batch/synthesis.md
Format as Markdown. Be specific — reference which sessions showed which patterns. Be concise — this is a summary, not a repetition of individual findings.
Read /tmp/session-review-batch/synthesis.md and present the full synthesis to the user.
If any individual session findings are particularly interesting, mention that the user can find per-session details in /tmp/session-review-batch/findings-N.md.